Dictionaries

See Patrick Hanks “Lexicography” chapter 3 of Mitkov, R. (ed.) The Oxford Handbook of Computational Linguistics, Oxford: OUP, 2004.

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Dictionaries/Lexicons

• Lexicography and the computer

• Corpus-based lexicography

• MRDs

• Dictionaries for NLP

• Thesauri: structured lexicons

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Computational lexicography

• Restructuring and exploiting human dictionaries for use by computer programs

• Using computational techniques to compile (new) dictionaries

• Focus on English (and other well established languages)

• Significant different issues for other languages, especially– Alphabetization and arrangement– Compilation from scratch for previously unstudied

languages

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Human dictionaries• Traditional view of what a “dictionary” is

– List of words, arranged (usually) alphabetically– Inclusion in dictionary lends authority, even proscriptively– Entry typically gives

• spelling ... alternate spellings• POS, morphology (if irregular)• core definition (using defining vocab?)• pronunciation (using own transcription)• etymology• examples of usage

– as justification for inclusion– as illustration of use (esp. learner’s dictionaries)

– Entry typically doesn’t give • help with spelling• morphology (if regular), especially derivational• subcategorization information• contrastive examples of use• indications of possible metaphorical extensions to meaning

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Human dictionaries

• Historically– bilingual dictionaries for translators– monolingual dictionary as (pre/proscriptive) definition

of language, often polemical– OED (1884-1928) first dictionary on purely descriptive

principle, relying on citations

• Deficiencies and difficulties– What to include? (neologisms, slang)– Inclusion of names– Differentiating senses

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Differentiating word senses

• Dictionaries disagree widely• Probably no right answer• General principles (look for excuse to split vs

look for reason to lump)• Keep related words of different POS together?• Etymology can be misleading (eg crane, pupil)• Metaphorical extension of original meaning –

how far do you go? (eg rose, bar)• Purpose of dictionary may help decide, eg

translation

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Citations

• Senses and uses identified by collecting examples of use– Sent in on “slips” by informants– Lexicographer’s job is to collate these

• Criteria for a new word (or new meaning)– Number of citations– Source of citations– Veracity of use

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Corpus-based dictionaries

• A collection of texts, usually collected with a specific purpose in mind

• British National Corpus, attempt to capture a synchronic picture of BrE of the late 1980s (100m words)

• COBUILD “Bank of English” dynamic “monitor” corpus used to help lexicographers identify/define usage

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Machine-readable dictionaries

• “Machine” means “computer”

• Dictionary stored in a format which makes it manipulable on a computer

• Originally, derived from MR version of print dictionary (from type-setter’s tapes)

• Now the other way round: data stored as a database from which hard copy can be printed (inter alia)

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MRDs - advantages

• Flexibility of access and presentation– Not bound to alphabetical listing– Information presented can be filtered– Can be searched as a database– Different versions (for different users, serving different

purposes) can be produced

• Increased storage capacity– More information can be stored, especially

• Implicit information can be made explicit• More examples, including “negative data”

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Lexicons for NLP

• Have to state everything we need to know about the word– Phonology: stress pattern, possible weak forms– Orthography: spelling alternatives, hyphenation– Morphology: inflectional paradigms, even if regular– Information about derivations– Syntax: Explicit information about subcategorization and

• eg syntactic/semantic features of arguments• Any special interpretation of tenses

– Lexical combinatorics: compounds, idioms– Semantics: definition, semantic features, semantic relations– Pragmatics: register, collocation, connotation

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Lexicons for NLP - example

• Information about derivations• Agentive derivation (-er) is very productive

– Usually means the actor doing the action of a verb, e.g. swimmer, dancer, killer

– Not available for some verbs, e.g. *knower, *cycler, *sayer though cf soothsayer, *hoper

– May have a specialised meaning instead of or as well as the derived meaning, e.g. revolver, computer, washer, hitter

– In some cases can mean the object undergoing the action (via ergative use of verb), e.g. taster

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Subcategorization• Words are assigned to categories (ie parts

of speech, POS), eg noun, verb– on basis of form, meaning, use

• Syntactic behaviour is predictable from (or determined by) category

• Within a category there are subcategories with specific patterns of behaviour, both syntactic and semantic, e.g.– transitive/intransitive verb direct object?

passivize?

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Subcategorization

• Subcat frames indicate complement patterns and preferences, e.g.– subj, obj, double obj, prep-obj, infinitival complement,

that complement etc– semantic features of complements, eg obj of eat

normally edible

• Subcat information can help to disambiguate– cf He told the man where the body was buried .– He found the place where the body was buried .

• Much of this info can be captured in general rules

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• Have to state everything we need to know about the word, though not necessarily explicitly– There can be rules to capture inheritance of

properties, e.g.• accomplishment + prog tense implies incompletion

• cf She was baking a cake when she dropped dead no cake

• She was stroking the cat when she dropped dead

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Exploiting human dictionaries in NLP

• In all NLP applications, lexicon is major bottleneck• Availability of MRD versions of human dictionaries

provided possible solution– Obviously, MRD gives list of words, and some information– Extract further information about verb frames by analysing the

examples– Identify semantic features from definitions eg a plant which..., a person who...– Identify hidden arguments eg to lock = to close sthg using a key cf He locked the door. The key was heavy. He emptied his pockets. *The key was heavy.

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Exploiting human dictionaries in NLP

• Generic information about a word and its usage can be derived from definitions in which it occurs:

Wine: alcoholic drink made from fermented juices, especially of grapesVintage: a season’s yield of wine from a vineyardRed wine: wine having a red colour derived from the skins of the grapes used ...Vineyard: an orchard where grapes are grown for the purpose of wine makingPinot noir: a dry red Californian table wineSake: Japanese rice wineClaret: a dry red Bordeaux or Bordeaux-like wineSherry: a sweet white wine from the Jerez region of SpainRiesling: a dessert wine made from white grapes grown historically in Germany ...

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Corpus-based lexicography revisited

• Similarly, analysis of real examples can reveal patterns of usage– Identify primary meaning: not always what

you’d expect (example of reckon)– Identify possible complementation patterns,

and their relative frequency

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Structured dictionaries

• Special type of dictionary in which words are grouped together according to their meaning: thesaurus

• Classic example Roget’s Thesaurus (1852)

• Structured vocabulary much used in field of terminology

• Also now a valuable resource for NLP: Miller’s (Princeton) WordNet (1985)